Abstract: Systems and methods for determining a sedentary state of a user are described. Sensor data is collected and analyzed to calculate metabolic equivalent of task (MET) measures for a plurality of moments of interest. Based on the MET measures and a time period for which the MET measures exceed a threshold value, it is determined whether the user is in a sedentary state. If the user is in the sedentary state, the user is provided a notification to encourage the user to perform a non-sedentary activity.

Abstract: The disclosure relates to methods, devices, and systems to identify a user of a wearable fitness monitor using data obtained using the wearable fitness monitor. Data obtained from motion sensors of the wearable fitness monitor and data obtained from heartbeat waveform sensors of the wearable fitness monitor may be used to identify the user.

Abstract: An activity monitoring device, methods and computer readable media are provided. The activity monitoring device includes a housing configured for attachment to a body part of a user and a display screen attached to the housing. Further included is a first sensor disposed in the housing for capturing motion of the activity monitoring device when attached to the body part of the user and a second sensor disposed in the housing for sampling a heart rate of the user. Memory is disposed in the housing for storing the motion captured by the first sensor and the heart rate sampled by the second sensor. A processor is disposed in the housing and is configured to determine a physical state of the user during a period of time. For motion that is below a threshold the processor identifies the physical state to be a sedentary state and for motion that is at or above the threshold the processor identifies the physical state to be an active state.

Abstract: In one embodiment, a method for creating a blood oxygen saturation (SpO2) value, the method comprises receiving one or more photoplethysmography (PPG) signals for SpO2 detection from one or more PPG sensors; receiving one or more PPG signals for characterizing a heart rate from the one or more PPG sensors; using the one or more PPG signals for SpO2 detection, forming one or more SpO2 datasets wherein the SpO2 datasets respectively comprise one or more noise components; removing the one or more noise components from the one or more SpO2 datasets that are inconsistent with a feature of the one or more PPG signals characterizing the heart rate to produce one or more filtered SpO2 datasets; and using the one or more filtered SpO2 datasets, creating and storing the SpO2.

Abstract: In one embodiment, an electronic device to be worn on a user's forearm includes a display and a set of one or more sensors that provide sensor data. In one aspect, a device may detect, using sensor data obtained from a set of sensors, that a first activity state of a user is active. The device may determine, while the first activity state is active, that the sensor data matches a watch check rule associated with the first activity state. Responsive to the detected match, the device may cause a change in visibility of the display.

Abstract: A method includes receiving location data of a monitoring device when carried by a user and receiving motion data of the monitoring device. The motion data is associated with a time of occurrence and the location data. The method includes processing the received motion data to identify a group of the motion data having a substantially common characteristic and processing the location data for the group of the motion data. The group of motion data by way of processing the location data provides an activity identifier. The motion data includes metric data that identifies characteristics of the motion data. The method includes transferring the activity identifier and the characteristics of the motion data to a screen of a device for display. The activity identifier being a graphical user interface that receives an input for rendering more or less of the characteristics of the motion data.

Abstract: One innovative aspect is directed to heart rate data collection. In some implementations, a circuit includes a light detector for generating a first electrical signal based on received light. The circuit includes a switching circuit, having a first and a second configuration, configured to receive a first voltage signal based on the first electrical signal and to switch among the first and the second configurations. The circuit includes first and second sampling circuits for sampling a value of the first voltage signal when the switching circuit is in the first configuration and second configurations, respectively. The circuit includes an ambient light cancellation circuit for generating a current signal to counter a first component of the first electrical signal when the first switching circuit is in the first configuration.

Abstract: The disclosure provides BMDs that have multiple device modes depending on operational conditions of the devices, e.g., motion intensity, device placement, and/or activity type. The device modes are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods for tracking physiological metrics using the BMDs are provided. In some embodiments, the process or the BMD applies a time domain analysis on data provided by a sensor of the BMD for a first activity, and applies a frequency domain analysis on the data for a second activity, which contributes to improved accuracy and speed of biometric data.

Abstract: The disclosure relates to methods, devices, and systems to identify a user of a wearable fitness monitor using data obtained using the wearable fitness monitor. Data obtained from motion sensors of the wearable fitness monitor and data obtained from heartbeat waveform sensors of the wearable fitness monitor may be used to identify the user.

Abstract: Systems and methods for determining a sedentary state of a user are described. Sensor data is collected and analyzed to calculate metabolic equivalent of task (MET) measures for a plurality of moments of interest. Based on the MET measures and a time period for which the MET measures exceed a threshold value, it is determined whether the user is in a sedentary state. If the user is in the sedentary state, the user is provided a notification to encourage the user to perform a non-sedentary activity.

Abstract: Techniques for automatic tracking of user data for exercises are disclosed. In one aspect, a method of operating a wearable device may involve determining, based on output of one or more biometric sensors, that a user of the wearable device has started an exercise and identifying a type of the exercise that the user has started based on comparing the output of the one or more biometric sensors to defined sensor data for a plurality of exercise types. The method may further include adjusting a global positioning system (GPS) receiver in response to determining the start of the exercise and based on the type of the exercise.

Abstract: In one embodiment, an electronic device to be worn on a user's forearm includes a display and a set of one or more sensors that provide sensor data. In one aspect, a device may detect, using sensor data obtained from a set of sensors, that a first activity state of a user is active. The device may determine, while the first activity state is active, that the sensor data matches a watch check rule associated with the first activity state. Responsive to the detected match, the device may cause a change in visibility of the display.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: Biometric monitoring devices, including various technologies that may be implemented in such devices, are discussed herein. Additionally, techniques for enhancing GPS speed and distance measurements based on step counts measured by a biometric monitoring device are provided. Such techniques may, in some implementations, involve blending two independently-measured data streams of raw distance traveled—one based on inter-coordinate GPS coordinate distances and one based on step count and stride length—using a Kalman filter to provide an enhanced-accuracy measurement of distance or speed traveled. In some other or additional implementations, distances or speeds calculated based on inter-coordinate GPS coordinate distances may be smoothed with a smoothing constant that is proportional to the variance in measured step count.

Abstract: Biometric monitoring devices, including various technologies that may be implemented in such devices, are discussed herein. Additionally, techniques, systems, and apparatuses are discussed herein for providing power-conserving techniques and systems for efficiently utilizing a GPS receiver are described. The positional fix frequency of the GPS receiver may, according to some implementations, be modified or adjusted between various levels according to data from one or more non-GPS sensors. Such non-GPS sensors may include, for example, ambient light intensity or spectrum sensors, accelerometers, gyroscopes, magnetometers, heart rate sensors, galvanic skin response sensors, infrared sensors, etc.

Abstract: A method and apparatus for capacitive off-wrist detection for wearable device are disclosed. In one aspect, the wearable device includes one or more biometric sensors including a capacitive sensor. The method may involve measuring, based on output of the capacitive sensor, a capacitance value indicative of proximity of the wearable device to a user. The method may also involve detecting a change in the capacitance value within a defined time interval, the change being greater than or equal to a threshold change indicative of the wearable device not being proximate to the user's skin. The method may further involve determining that the wearable device has been removed from the user in response to detecting that the change in the capacitance value within the defined time interval is greater than or equal to the threshold change.

Abstract: The disclosure provides BMDs that have multiple device modes depending on operational conditions of the devices, e.g., motion intensity, device placement, and/or activity type. The device modes are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods for tracking physiological metrics using the BMDs are provided. In some embodiments, the process or the BMD applies a time domain analysis on data provided by a sensor of the BMD when the data has a high signal (e.g., high signal-to-noise ratio), and applies a frequency domain analysis on the data when the data has a low signal, which contributes to improved accuracy and speed of biometric data.

Abstract: A method includes receiving location data of a monitoring device when carried by a user and receiving motion data of the monitoring device. The motion data is associated with a time of occurrence and the location data. The method includes processing the received motion data to identify a group of the motion data having a substantially common characteristic and processing the location data for the group of the motion data. The group of motion data by way of processing the location data provides an activity identifier. The motion data includes metric data that identifies characteristics of the motion data. The method includes transferring the activity identifier and the characteristics of the motion data to a screen of a device for display. The activity identifier being a graphical user interface that receives an input for rendering more or less of the characteristics of the motion data.

Abstract: Biometric monitoring devices, including various technologies that may be implemented in such devices, are discussed herein. Additionally, techniques for utilizing gyroscopes in biometric monitoring devices are provided. Such techniques may, in some implementations, involve obtaining swimming metrics regarding stroke cycle count, lap count, and stroke type. Such techniques may also, in some implementations, involve obtaining performance metrics for bicycling activities.

Abstract: An activity monitoring device accumulates a count of steps taken as an individual bearing the activity monitoring device travels by foot along a route, and wirelessly acquires positioning data from a discrete global-positioning device corresponding to a sequence of locations along the route. The activity monitoring device iteratively revises an estimated stride length of the individual based on the positioning data and the step count and iteratively updates a travel distance on a display of the activity monitoring device according to the step count and the estimated stride length as the individual proceeds from one location in the sequence to the next.